Abstract: Abstract: Pear as one of the most favored fruit， its total acid content would has a great influnce on pear’s taste and quality， so the application of non-destructive assessment of total acid content in pears shows promising prospects. In this study， the near-infrared spectral data of 240 mature pear samples in northern Jiangxi were collected， take 180 random pear samples as the calibration set and 60 unknown samples as the prediction set. The study and analysis were conducted using 1401 wavelength points in the range of 400~1800 nm， after eliminating noise at the beginning and end of the spectrum. Original spectral data were preprocessed by SG smoothing method and baseline offset correction method， through the Partial Least Squares Regression mathematical model to determine the SG smoothing method has the most significant pretreatment of the original spectral; competitive adaptive reweighted sampling （CARS） and successive projections algorithm （SPA） are used to extract spectral characteristic wavelengths， meanwhile， combining Partial Least Squares Regression and Least Square Support Vector Machine analysis methods to establish the prediction model of total acid content， among them， the CARS+LS-SVM prediction model has the best prediction effect on the total acid content of pear， the R2p value was 0.901， the RPD value was 2.911. Research shows that visible near-infrared spectroscopy is a method to detect the total acid content of pear， combined with the CARS+LS-SVM prediction model， the quantitative detection of pear total acid content can be realized.

Key words: Key words: non-destructive examination, visible-near infrared spectroscopy, feature selection, pear, total acid